1. Field of the Invention
The present invention relates to multimedia information retrieval and more particularly to automatic news story classification and relevant story grouping.
2. Discussion of the Prior Art
Video organization is an important step in content-based indexing of video archives. The objective of video organization is to capture the semantic structure of a video in a form which is meaningful to the user.
One proposed method of classifying video content includes manually indexing content in order to achieve a desirable quality of classification and grouping. However, this method can be time consuming and expensive.
Another proposed method of content classification is the Multimedia Video News Organizer, written by Siemens Corporate Research, Inc. The organizer automatically processes video, creating multimedia video summaries, while providing interfaces for verification, correction, and augmentation of the automatically generated story segments and extracted multimedia content. The interface can be provided by various functions, for example, Java scripts including TreeCC.java, ShotTree.java, and TreeOrg.java. These functions are interactive browsing interfaces that allow a user to revise automatically constructed story units. According to the automatic video summary generation method, closed-captioned text often lags behind the corresponding spoken words. Therefore, a method of closed-captioned text alignment is provided.
Closed-captioning is transmitted with a television signal, embedded in the line 21 data area in the vertical blanking interval. Captioning can also be supplied over, for example, the Internet. Closed-captioning has been used in at least three different areas: to aid hearing-impaired television viewers; to convey content in noisy environments; and aid in educational settings, particularly in learning languages.
Presently, no system or method is known to exist using majority voting or likelihood summation methods for classifying content according to a textual signal. Therefore, a need exists for an automatic content classification and relevant content grouping.